What is the benefit of using pre-trained models in NLP?

Using pre-trained models in NLP offers the significant benefit of being fine-tuned on smaller datasets. Pre-trained models, such as those based on transformer architectures like BERT or GPT, have already been trained on vast amounts of data to capture language nuances, patterns, and contextual understanding. This extensive training allows these models to generalize well to a variety of tasks.

When faced with a specific NLP task, fine-tuning these models on smaller, domain-specific datasets becomes a much more efficient approach than training a model from scratch. This not only saves time and computational resources but also leverages the knowledge already embedded in the pre-trained model, often leading to better performance and quicker convergence.

The other potential options do not capture the main advantages of pre-trained models effectively. While they do not necessarily require extensive re-training data, they still need some amount of task-specific data to adjust parameters for the specific task. The speed at which models run can vary depending on many factors, so pre-trained models are not inherently faster than all other types. Moreover, pre-trained models are versatile and can be adapted to various types of data, including textual, visual, and audio data, so they do not solely work with textual input.